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Top-k Matching Queries for Filter-Based Profile Matching in Knowledge Bases

Part of the Lecture Notes in Computer Science book series (LNISA,volume 9828)

Abstract

Finding the best matching job offers for a candidate profile or, the best candidates profiles for a particular job offer, respectively constitutes the most common and most relevant type of queries in the Human Resources (HR) sector. This technically requires to investigate top-k queries on top of knowledge bases and relational databases. We propose in this paper a top-k query algorithm on relational databases able to produce effective and efficient results. The approach is to consider the partial order of matching relations between jobs and candidates profiles together with an efficient design of the data involved. In particular, the focus on a single relation, the matching relation, is crucial to achieve the expectations.

Keywords

  • Relational Database
  • Matching Relation
  • Applicant Profile
  • Minimum Match
  • Profile Match

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The research reported in this paper was supported by the Austrian Forschungsförderungsgesellschaft (FFG) for the Bridge project “Accurate and Efficient Profile Matching in Knowledge Bases” (ACEPROM) under contract [FFG: 841284]. The research reported in this paper has been supported by the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry of Science, Research and Economy, and the Province of Upper Austria in the frame of the COMET center SCCH [FFG: 844597].

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Correspondence to Alejandra Lorena Paoletti .

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Paoletti, A.L., Martinez-Gil, J., Schewe, KD. (2016). Top-k Matching Queries for Filter-Based Profile Matching in Knowledge Bases. In: Hartmann, S., Ma, H. (eds) Database and Expert Systems Applications. DEXA 2016. Lecture Notes in Computer Science(), vol 9828. Springer, Cham. https://doi.org/10.1007/978-3-319-44406-2_23

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  • DOI: https://doi.org/10.1007/978-3-319-44406-2_23

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